{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "f = open('task_3.1_zhihu_timeline_answer.csv',encoding='UTF-8') \n",
    "zhihu_data_answer = pd.read_csv(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "       类型                 id   标签       回答id  回答点赞数  回答评论数  回答感谢数    回答观看数  \\\n0  answer  12_1574824946.178  二次元  734170020   4859    656    564  1120728   \n1  answer   13_1574824946.45   生活  629465796  46827   1594  12491  4517999   \n2  answer  14_1574824946.791   高考  344182099  25739   2465   5072  1575771   \n\n       回答创建时间       问题id                  问题题目  问题回答数  问题关注数  评论数        创建时间  \n0  1561974232   62972819  你们见过最好看的 coser 长什么样？   1607  30094   30  1501051575  \n1  1553236233   63894266          生活中有哪些残忍的真相？   2505  57217   50  1502701040  \n2  1521306846  266837866  你们是怎么考上 985 的，每科多少分？   3527  28092   64  1518015871  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>类型</th>\n      <th>id</th>\n      <th>标签</th>\n      <th>回答id</th>\n      <th>回答点赞数</th>\n      <th>回答评论数</th>\n      <th>回答感谢数</th>\n      <th>回答观看数</th>\n      <th>回答创建时间</th>\n      <th>问题id</th>\n      <th>问题题目</th>\n      <th>问题回答数</th>\n      <th>问题关注数</th>\n      <th>评论数</th>\n      <th>创建时间</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>answer</td>\n      <td>12_1574824946.178</td>\n      <td>二次元</td>\n      <td>734170020</td>\n      <td>4859</td>\n      <td>656</td>\n      <td>564</td>\n      <td>1120728</td>\n      <td>1561974232</td>\n      <td>62972819</td>\n      <td>你们见过最好看的 coser 长什么样？</td>\n      <td>1607</td>\n      <td>30094</td>\n      <td>30</td>\n      <td>1501051575</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>answer</td>\n      <td>13_1574824946.45</td>\n      <td>生活</td>\n      <td>629465796</td>\n      <td>46827</td>\n      <td>1594</td>\n      <td>12491</td>\n      <td>4517999</td>\n      <td>1553236233</td>\n      <td>63894266</td>\n      <td>生活中有哪些残忍的真相？</td>\n      <td>2505</td>\n      <td>57217</td>\n      <td>50</td>\n      <td>1502701040</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>answer</td>\n      <td>14_1574824946.791</td>\n      <td>高考</td>\n      <td>344182099</td>\n      <td>25739</td>\n      <td>2465</td>\n      <td>5072</td>\n      <td>1575771</td>\n      <td>1521306846</td>\n      <td>266837866</td>\n      <td>你们是怎么考上 985 的，每科多少分？</td>\n      <td>3527</td>\n      <td>28092</td>\n      <td>64</td>\n      <td>1518015871</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 3
    }
   ],
   "source": [
    "zhihu_data_answer.head(3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "answer_like=zhihu_data_answer['回答点赞数'].tolist()\n",
    "answer_thanks=zhihu_data_answer['回答感谢数'].tolist()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False#显示中文\n",
    "plt.xlabel('回答点赞数')\n",
    "plt.ylabel('回答感谢数')\n",
    "plt.scatter(answer_like,answer_thanks,c='r',marker='.')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "question_answer=zhihu_data_answer['问题回答数'].tolist()\n",
    "question_attention=zhihu_data_answer['问题关注数'].tolist()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('问题回答数')\n",
    "plt.ylabel('问题关注数')\n",
    "plt.scatter(question_answer,question_attention,c='r',marker='.')\n",
    "plt.show()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "f = open('task_3.2_zhihu_timeline_article.csv',encoding='UTF-8') \n",
    "zhihu_data_article = pd.read_csv(f)\n",
    "f.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "        类型               总 id  关键词     文章 id  文章点赞数  文章评论数  \\\n0  article  14_1574825136.990  李小璐  90620737    774    267   \n1  article  14_1574825137.717  主持人  90576308  25365   1157   \n2  article  13_1574825137.707   刘涛  92353372    339     65   \n\n                  文章题目（文章没有感谢数）    文章观看数      文章创建时间  \n0  “李小璐出轨视频曝光7天后，我识破了令人胆寒的爱情真相”   245037  1573089756  \n1     谢娜与董卿同台被“碾压”: 文化断层到底有多可怕?  1072054  1573047546  \n2                       刘涛，你够了！   215292  1574051699  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>类型</th>\n      <th>总 id</th>\n      <th>关键词</th>\n      <th>文章 id</th>\n      <th>文章点赞数</th>\n      <th>文章评论数</th>\n      <th>文章题目（文章没有感谢数）</th>\n      <th>文章观看数</th>\n      <th>文章创建时间</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>article</td>\n      <td>14_1574825136.990</td>\n      <td>李小璐</td>\n      <td>90620737</td>\n      <td>774</td>\n      <td>267</td>\n      <td>“李小璐出轨视频曝光7天后，我识破了令人胆寒的爱情真相”</td>\n      <td>245037</td>\n      <td>1573089756</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>article</td>\n      <td>14_1574825137.717</td>\n      <td>主持人</td>\n      <td>90576308</td>\n      <td>25365</td>\n      <td>1157</td>\n      <td>谢娜与董卿同台被“碾压”: 文化断层到底有多可怕?</td>\n      <td>1072054</td>\n      <td>1573047546</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>article</td>\n      <td>13_1574825137.707</td>\n      <td>刘涛</td>\n      <td>92353372</td>\n      <td>339</td>\n      <td>65</td>\n      <td>刘涛，你够了！</td>\n      <td>215292</td>\n      <td>1574051699</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 9
    }
   ],
   "source": [
    "zhihu_data_article.head(3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "article_like=zhihu_data_article['文章点赞数'].tolist()\n",
    "article_comment=zhihu_data_article['文章评论数'].tolist()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('文章点赞数')\n",
    "plt.ylabel('文章评论数')\n",
    "plt.scatter(article_like,article_comment,c='r',marker='.')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD6CAYAAAC1W2xyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAACkZJREFUeJzt3U+o3Xdax/HPYzJN0sQZE3qJFMzEQDf+oc5w0QaqpGKFLkahIA7quFAIwiQuupCxU+koEqVIFYROjMRB0E1dKCMqZCVG6CAJ7lTQRYoUChkbW5vSjTwucqZJk3tzzyQ3Ocnj6wUXzn1+5/5+X7p498vvnJNT3R0A5viOVS8AgO0l7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwyzcxUXfeyxx/rw4cOruDTAQ+vixYvf7O61rZ63krAfPnw4Fy5cWMWlAR5aVfXmMs9zKwZgGGEHGEbYAYYRdoBhhB1gGGGHhZMnT2b37t2pquzevTsnT55c9ZLgjgg75FrUT58+nVOnTuXq1as5depUTp8+Le48lGoVX423vr7e3sfOg2T37t05depUXnjhhY9mr776al588cV8+OGHK1wZXFdVF7t7fcvnCTskVZWrV6/m0Ucf/Wj2wQcfZO/evfG9wDwolg27WzGQZNeuXTl9+vTHZqdPn86uXbtWtCK4c3bsjFZV9+U6dvXcD3bskGvBXfbnxIkTH+3Qd+3alRMnTiz9t/AgsWOHm1SVWPNAsmMH+H9K2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYJjbhr2q9lfV31bVhar6o8XsbFW9UVUv3fC8W2YArMZWO/YvJPnzxbdif2dV/VqSHd19NMmRqnqiqp6/eXaP1wzAbWwV9v9K8gNV9V1JvifJ9yZ5fXHsXJKnkxzbYHaLqjq+2PlfuHz58t2uG4BNbBX2f0zy6SS/muRfkzyS5K3FsXeSHEyyd4PZLbr7THevd/f62tra3a4bgE1sFfaXk/xKd/9Wkn9L8nNJ9iyO7Vv8/fsbzABYka0ivD/JD1bVjiQ/kuR3c/1Wy5NJLiW5uMEMgBXZucXx30nytVy7HfNGkt9Pcr6qHk/yXJKnkvQGMwBW5LY79u7+p+7+/u7e193Pdvd7ufZi6TeSPNPd7240u9eLBmBzW+3Yb9HdV3L9XTCbzgBYDS90Agwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwwj7ADDCDvAMMIOMIywAwyzdNir6rWq+tzi8dmqeqOqXrrh+C0zAO6/pcJeVT+a5Lu7+6+r6vkkO7r7aJIjVfXERrN7uGYAbmPLsFfVJ5L8cZJLVfXTSY4leX1x+FySpzeZAbACy+zYfzHJvyR5JckPJ/likrcWx95JcjDJ3g1mH1NVx6vqQlVduHz58t2uG4BNLBP2zyQ5091vJ/mzJP+QZM/i2L7FOd7fYPYx3X2mu9e7e31tbe2uFw7AxpYJ+38kObJ4vJ7kcK7fankyyaUkFzeYAbACO5d4ztkkf1JVn0/yiVy7n/71qno8yXNJnkrSSc7fNANgBbYMe3f/T5KfuXFWVceSPJvkle5+d7MZAPffMjv2W3T3lVx/F8ymMwDuP588BRhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGWSrsVXWwqv558fhsVb1RVS/dcPyWGQCrseyO/feS7Kmq55Ps6O6jSY5U1RMbze7VYgHY2pZhr6ofT3I1ydtJjiV5fXHoXJKnN5kBsCK3DXtVPZLkN5J8aTHam+StxeN3khzcZLbRuY5X1YWqunD58uW7XTcAm9hqx/6lJK91938vfn8/yZ7F432Lv99odovuPtPd6929vra2dnerBmBTW4X9J5J8sar+PskPJflcrt9qeTLJpSQXN5gBsCI7b3ewu3/sW48Xcf+pJOer6vEkzyV5KklvMANgRZZ+H3t3H+vu93LtxdJvJHmmu9/daHYvFgoHDhxIVd3znyT3/BoHDhxY8X9NJrvtjn0j3X0l198Fs+kMttuVK1fS3atexrb41v9A4F7wyVOAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYJgtw15Vn6qqv6uqc1X1l1X1SFWdrao3quqlG553ywyA+2+ZHfvPJ3m1u38yydtJPp9kR3cfTXKkqp6oqudvnt27JQNwOzu3ekJ3v3bDr2tJfiHJHyx+P5fk6SSfSfL6TbN/v/E8VXU8yfEkOXTo0F0tGoDNLX2PvaqOJtmf5D+TvLUYv5PkYJK9G8w+prvPdPd6d6+vra3d1aIB2NxSYa+qA0n+MMkvJXk/yZ7FoX2Lc2w0A2AFlnnx9JEkf5Hk17v7zSQXc+1WS5I8meTSJjMAVmDLe+xJfjnJZ5N8uaq+nORrSb5QVY8neS7JU0k6yfmbZgCswDIvnn41yVdvnFXV15M8m+SV7n53MTt28wyA+2+ZHfstuvtKrr8LZtMZAPefFzkBhhF2gGGEHWAYYQcY5o5ePIVV6Jc/mXzlU6texrbolz+56iUwmLDz0KjffC/dveplbIuqSn9l1atgKrdiAIYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhhB1gGGEHGEbYAYYRdoBhdq56AfDtqKpVL2Fb7N+/f9VLYLBtDXtVnU3yfUn+prt/ezvPDd19X65TVfftWnAvbNutmKp6PsmO7j6a5EhVPbFd5wZgedt5j/1YktcXj88leXobzw3AkrbzVszeJG8tHr+T5LM3Hqyq40mOJ8mhQ4e28bKwuTu9J//t/p1bNzxItnPH/n6SPYvH+24+d3ef6e717l5fW1vbxsvC5rr7vvzAg2Q7w34x12+/PJnk0jaeG4AlbeetmL9Kcr6qHk/yXJKntvHcACxp23bs3f1err2A+o0kz3T3u9t1bgCWt63vY+/uK7n+zhgAVsA/KQAwjLADDCPsAMMIO8AwtYoPV1TV5SRv3vcLw3IeS/LNVS8CNvDp7t7yE54rCTs8yKrqQnevr3odcKfcigEYRtgBhhF2uNWZVS8A7oZ77ADD2LEDDCPscIOqOlhV51e9Drgbwg4LVbU/yZ/m2reBwUNL2OG6/03ys0neW/VC4G5s6z/bCw+zxXcK3PH3pMKDwo4dYBhhBxhG2AGG8QElgGHs2AGGEXaAYYQdYBhhBxhG2AGGEXaAYYQdYJj/A/Q/wzwf9wN5AAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "article_like_less_than_1000=[x for x in article_like if x < 1000]\n",
    "plt.boxplot(article_like_less_than_1000)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}